Fundamentals of Deep Learning: NVIDIA DLI Certification Workshop for Academia

https://www.nvidia.com/en-eu/training/instructor-led-workshops/fundamentals-of-deep-learning/

Deep Learning with PyTorch Workshop

In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

Learning Objectives

By participating in this workshop, you’ll:

• Learn the fundamental techniques and tools required to train a deep learning model
• Gain experience with common deep learning data types and model architectures
• Enhance datasets through data augmentation to improve model accuracy
• Leverage transfer learning between models to achieve efficient results with less data and computation
• Build confidence to take on your own project with a modern deep learning framework

Download workshop datasheet (PDF, 318 KB)

Preparation for the Workshop

• Fill in the form at https://forms.gle/8iNZN3PToUh6iveC9 to gain access codes to the event (will be emailed shortly before it)
• Install Google Chrome or Mozilla Firefox to use the NVIDIA DLI Environment
• Create an account at https://learn.nvidia.com/

Mechanics of Deep Learning
Explore the fundamental mechanics and tools involved in successfully training deep neural networks:

• Train your first computer vision model to learn the process of training
• Introduce convolutional neural networks to improve accuracy of predictions in vision applications
• Apply data augmentation to enhance a dataset and improve model generalization

Pre-trained Models
Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data:

• Integrate a pre-trained image classification model to create an automatic doggy door
• Leverage transfer learning to create a personalized doggy door that only lets in your dog

Assessment Challenge: Image Classification
Apply computer vision to create a model that distinguishes between fresh and rotten fruit:

• Create and train a model that interprets color images
• Build a data generator to make the most out of small datasets
• Improve training speed by combining transfer learning and feature extraction
• Discuss advanced neural network architectures and recent areas of research where students can further improve their skills

Final Review

• Review key learnings and answer questions
• Complete the assessment and earn a certificate
• Complete the workshop survey
• Learn how to set up your own AI application development environment